COVID-19: Cell phone data reveals ‘superspreader’ venues
Written by James Kingsland on November 12, 2020 — Fact checked by Anna Guildford, Ph.D.
A model of SARS-CoV-2 transmission suggests that a small number of venue types, such as restaurants, hotels, and religious venues, account for the majority of infections. The model also helps explain why infections disproportionately affect people living in deprived areas.
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Almost a year since the start of the COVID-19 pandemic in Wuhan, China, there remains considerable uncertainty about the safest ways to restore economic and social life to something resembling normality.
According to the creators of the new model at Stanford University, CA, and Northwestern University in Chicago, IL, the model provides a tool for identifying high risk venues and testing alternative paths out of lockdown.
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Computer scientists and medical researchers collaborated on the model, which used anonymized location data from cell phone networks to reveal the movements of 98 million people in the United States between March 1 and May 2, 2020.
The team modeled the number of infections occurring hour by hour at around 553,000 venues, which they grouped into 20 categories according to use. They also factored in the floor space of each venue.
Their simulations accurately predicted daily confirmed infections in 10 of the largest metropolitan areas, including Chicago, New York City, and San Francisco.
The model suggests that reopening gyms, full-service restaurants, cafes, hotels, and religious venues leads to the largest surge in infections, due to the high densities of people and their long lengths of stay.
A relatively small number of these “superspreader” venues account for the majority of new infections, according to the model.
For example, the model found that 10% of all the venues in the Chicago metropolitan area accounted for 85% of all infections.
Capping numbers of visitors
On the upside, the model indicates that capping the number of people allowed into venues at any one time is more effective and less disruptive than uniformly reducing everyone’s freedom of movement.
For instance, the model predicts that limiting the occupancy of a venue to 20% of its maximum capacity reduces new infections by more than 80%.
Because people are likely to respond by spreading their visits more thinly throughout the day, however, the measure reduces the total number of visits by a relatively modest 42%.
“[O]ne can achieve a disproportionately large reduction in infections with a small reduction in visits,” the researchers write in their paper describing the model, which appears in the journal Nature. “Precise interventions like these may be more effective than less targeted measures while incurring substantially lower economic costs.”
“Our work highlights that it doesn’t have to be all or nothing,” said senior author Jure Leskovec of Stanford University at a press conference held to announce the findings.
He added, “We can choose different levels for different types of places, and our model provides a tool for policymakers to basically navigate these trade-offs and make the right decisions for them.”